Publication
All Eyes on the Workflow: Automated and Efficient Event Discovery from Video Streams
Marco Pegoraro; Jonas Seng; Dustin Heller; Wil M. P. van der Aalst; Kristian Kersting
In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2604.22476, Pages 1-17, arXiv, 2026.
Abstract
Disciplines such as business process management and pro-
cess mining aid organizations by discovering insights about processes
on the basis of recorded event data. However, an obstacle to process
analysis is data multi-modality: for instance, data in video form are not
directly interpretable as events. In this work, we present SnapLog, an ap-
proach to extract event data from videos by converting frames to feature
vectors using image embeddings and performing temporal segmentation
through frame-wise similarity matrices. A generalized few-shot classifica-
tion is then used to assign labels to the video segments, yielding labeled,
timestamped sub-sequences of frames that are interpretable as events.
Conventional process mining techniques can be used to analyze the re-
sulting data. We show that our approach produces logs that accurately
reflect the process in the videos.